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191 results for “Inflation”
Supplementary Data: Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT
<p><strong>Description of Supplementary Data</strong></p> <p>This record contains the samples used to create the figures (excluding validation and prior dependence plots) and to derive most of the results in Hoof et al., <em>“Axion global fits with Peccei-Quinn symmetry breaking before inflation using GAMBIT”</em> (available on the <a href="https://arxiv.org/abs/1810.07192">arXiv</a>). Please contact the authors if you are interested in other samples, YAML files or plotting scripts.<br> <br> This record consists of</p> <ul> <li>21 <code>YAML</code> files (6 for <code>T-Walk</code>, 15 for <code>Diver</code>). Running <code>./gambit -f path/to/YAML/file.yaml</code> in the GAMBIT directory will start the scan. However, most users might want to adjust the output file name and directory as well as the settings for the samplers to their systems.</li> <li>21 <code>hdf5</code> files (6 for <code>T-Walk</code>, 15 for <code>Diver</code>). These files contain the actual samples and were compressed using the <code>tar</code> format.</li> <li>Two example <code>pip</code> files (<code>2_QCDAxion_10M1.pip</code> for <code>Diver</code> samples, <code>2_QCDAxion_3041.pip</code> for <code>T-Walk</code> samples) for producing plots from the corresponding <code>hdf5</code> files, using <a href="https://github.com/patscott/pippi"><code>pippi</code></a> and <code>functions.py</code>.</li> </ul> <p>The files follow the naming scheme <code>V_ModelName_[S][C][I][R][E]</code> plus one of the extensions <code>.yaml</code>, <code>.hdf5.tar.gz</code>, or <code>.pip</code>.</p> <ul> <li><code>V</code>: This internal version number can be ignored, but should be quoted when asking for help with the plotting scripts</li> <li><code>ModelName</code>: Corresponds to the axion models in the paper (<em>GeneralALP</em>, <em>QCDAxion</em>, <em>DFSZAxion_I</em>, <em>DFSZAxion_II</em>, <em>KSVZAxion</em>)</li> <li><code>S</code>: Scanner (<code>S=1</code>: <code>Diver</code>, <code>S=3</code>: <code>T-Walk</code>)</li> <li><code>C</code>: Switch to include (<code>C=1</code>) or exclude (<code>C=0</code>) the White Dwarf cooling hints</li> <li><code>I</code>: Setting for the initial misalignment angle <em>θ<sub>i</sub></em> (<code>I=4</code>: flat prior on <em>θ<sub>i</sub></em> with values in [-3.1415, 3.1415]). <code>I=M</code> is used to indicate that the file includes merged samples from other scans in addition to the corresponding <code>I=4</code> scan.</li> <li><code>R</code>: Setting for the DM relic density likelihood (<code>R=1</code>: upper limit, <code>R=2</code>: matching the DM density)</li> <li><code>E</code>: Extra digit for the anomaly ratio <em>E/N</em>; only for <em>KSVZAxion</em> models (<code>E=1</code>: 0, <code>E=2</code>, 2/3, <code>E=3</code>: 5/3, <code>E=4</code>: 8/3), <em>DFSZAxion-I</em> models (<code>E=1</code>: 8/3), <em>DFSZAxion-II</em> models (<code>E=2</code>: 2/3), or some <em>GeneralALP</em> files (<code>E=a</code>: “QCD-like setting” with <em>β</em> = 7.94, <em>T<sub>crit</sub></em> = 147 MeV; <code>E=b</code>: “Simple ALP-like setting” with <em>β</em> = 0, <em>T<sub>crit</sub></em> irrelevant)</li> </ul> <p>For convenience, we provide a mapping between the figures in the paper and the <code>hdf5</code> files:</p> <ul> <li>Fig. 1: none</li> <li>Figs 2 - 11: Validation plots</li> <li>Figs 12 + 13: 2_GeneralALP_10M2</li> <li>Fig. 14: 2_GeneralALP_10M2a, 2_GeneralALP_10M2b</li> <li>Fig. 15: 2_QCDAxion_10M1, 2_QCDAxion_10M2</li> <li>Fig. 16: 2_QCDAxion_3041, 2_QCDAxion_3042</li> <li>Figs 17 + 18: 2_QCDAxion_10M1, 2_QCDAxion_10M2, 2_QCDAxion_30M1, 2_QCDAxion_30M2</li> <li>Fig. 19: 3_KSVZAxion_10M11, 3_KSVZAxion_10M12, 3_KSVZAxion_10M13, 3_KSVZAxion_10M14, 3_DFSZAxion_I_10M11, 3_DFSZAxion_II_10M12</li> <li>Fig. 20: 2_QCDAxion_10M1, 3_KSVZAxion_10M11, 3_KSVZAxion_10M12, 3_KSVZAxion_10M13, 3_KSVZAxion_10M14, 3_DFSZAxion_I_10M11, 3_DFSZAxion_II_10M12</li> <li>Fig. 21: 2_QCDAxion_11M1, 2_QCDAxion_11M2</li> <li>Fig. 22: 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Figs 23 + 24: 2_QCDAxion_3041, 2_QCDAxion_3042, 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Fig. 25: 2_QCDAxion_3141, 2_QCDAxion_3142</li> <li>Fig. 26: 2_QCDAxion_11M1, 3_DFSZAxion_I_11M11, 3_DFSZAxion_II_11M12</li> <li>Fig. 27: 2_QCDAxion_3141, 3_DFSZAxion_I_31411, 3_DFSZAxion_II_31412</li> <li>Fig. 28: Validation plot</li> <li>Fig. 29: Prior dependence plot</li> </ul> <p>A few caveats to keep in mind:</p> <ul> <li>The YAML files are designed to work with <code>GAMBIT 1.3.1</code>, and the pip files are tested with <code>pippi 2.1</code>, commit 1a08644. They may or may not work with later versions of either software (these working versions/commits can always be obtained via the <code>git</code> history).</li> <li>The <code>pip</code> files will produce an approximately complete, but very basic version of plots in the paper. Re-creating all the plots in the paper requires various manual, undocumented interventions such as additions, deletions and combination of the plotting scripts created by <code>pippi</code>. Users wishing to reproduce the more advanced plots in the paper should contact the authors for tips, scripts, or experiment for themselves.</li> </ul>
Accessible Oceans: Auditory Display. Longterm Axial Seamount Inflation Record
<p>The thirteen tracks make up an auditory display of the Longterm Axial Seamount Inflation Record. The tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/MViV0dJLZjJpFXEHN8EA">listen online here</a>.</p> <p>The display leverages data from NOAA PMEL that extend the record of the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) data back to 1997. This audio display focuses on the long-term pattern observed by bottom pressure recorders where the seafloor inflates (lifts), then an eruption event occurs, and the seafloor drops.</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Inference products for "Finite inflation in curved space"
<p>These are the MCMC and nested sampling inference products and input files that were used to compute results for the paper <strong>"Finite inflation in cuved space"</strong> by <strong>L. T. Hergt</strong>, <strong>F. J. Agocs</strong>, <strong>W. J. Handley</strong>, <strong>M. P. Hobson</strong>, and <strong>A. N. Lasenby</strong> from 2022.</p> <p>Example plotting scripts (as <span>\(\texttt{.ipynb}\)</span> or as <span>\(\texttt{.html}\)</span> files) and figures from the paper are included to demonstrate usage.</p> <p> </p> <p>We used the following python packages for the genertion of MCMC and nested sampling chains:</p> <table> <tbody><tr> <th>Package</th> <th>Version</th> </tr> </tbody><tbody> <tr> <td>anesthetic</td> <td>2.0.0b12</td> </tr> <tr> <td>classy</td> <td>2.9.4</td> </tr> <tr> <td>cobaya</td> <td>3.0.4</td> </tr> <tr> <td>GetDist</td> <td>1.3.3</td> </tr> <tr> <td>primpy</td> <td>2.3.6</td> </tr> <tr> <td>pyoscode</td> <td>1.0.4</td> </tr> <tr> <td>pypolychord</td> <td>1.20.0</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Filename conventions:</p> <ul> <li><span>\(\texttt{mcmc}\)</span>: MCMC run</li> <li><span>\(\texttt{pcs#d####}\)</span>: PolyChord run (in synchronous mode) with <span>\(\texttt{#d}\)</span> repeats per parameter block (where <span>\(\texttt{d}\)</span> is the number of parameters in that block) and with <span>\(\texttt{####}\)</span> live points.</li> <li><span>\(\texttt{_cl_hf}\)</span>: Using Boltzmann theory code CLASS with nonlinearities code halofit.</li> <li><span>\(\texttt{_p18}\)</span>: Using Planck 2018 CMB data.</li> <li><span>\(\texttt{_TTTEEE}\)</span>: Using the high-l TTTEEE likelihood.</li> <li><span>\(\texttt{_TTTEEElite}\)</span>: Using the lite version of the high-l TTTEEE likelihood.</li> <li><span>\(\texttt{_lowl_lowE}\)</span>: Using the low-l likelihoods for temperature and E-modes.</li> <li><span>\(\texttt{_BK15}\)</span>: Using data from the 2015 observing season of Bicep2 and the Keck Array.</li> <li><span>\(\texttt{lcdm}\)</span>: Concordance cosmological model called LCDM (standard 6 cosmological sampling parameters, no tensor perturbations, zero spatial curvature)</li> <li><span>\(\texttt{_r}\)</span>: Extension with a variable tensor-to-scalar ratio <span>\(r\)</span>.</li> <li><span>\(\texttt{_omegak}\)</span>: Extension with a variable curvature density parameter <span>\(\Omega_K \)</span>.</li> <li><span>\(\texttt{_H0}\)</span>: Sampling over <span>\(H_0\)</span> instead of <span>\(\theta_\mathrm{s}\)</span>.</li> <li><span>\(\texttt{_omegakh2}\)</span>: Extension with a variable curvature density parameter, but sampling over <span>\(H_0\)</span> instead of <span>\(\theta_\mathrm{s}\)</span> and over <span>\(\omega_K\equiv\Omega_Kh^2\)</span> instead of <span>\(\Omega_K \)</span>.</li> <li><span>\(\texttt{_mn2}\)</span>: Using a quadratic monomial potential for the computation of the primordial universe.</li> <li><span>\(\texttt{_nat}\)</span>: Using the natural inflation potential for the computation of the primordial universe.</li> <li><span>\(\texttt{_stb}\)</span>: Using the Starobinsky potential for the computation of the primordial universe.</li> <li><span>\(\texttt{_AsfoH}\)</span>: Using the primordial sampling parameters {`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`}.</li> <li><span>\(\texttt{_perm}\)</span>: Assuming a permissive reheating scenario.</li> </ul> <p> </p> <p> </p>
A Review Of Metaheuristics in Fuzzy Time Series Applied To Zero Inflated Datasets
<p>The manufacturing efficiency reflects directly on the use of natural resources and leads to a higher environmental impact than needed. Efficiency of an industry can be achieved in many ways, but it always starts with demand management. However some products have erratic and irregular demand patterns as the nature of the usage varies, and this often leads to zero inflated demand datasets, said datasets are difficult to forecast due to the nature of traditional models which usually use moving averages, state of the art machine learning models can achieve good results but use too much data for training. Under this background, this paper investigates the Fuzzy Time Series models and how it evolved from its inception to present time and how the usage of metaheuristics can help with forecasting demand on a small dataset with a high count of zeros, then applies the techniques to other zero inflated dataset to verify its generalization capabilities. Finally another model is applied as comparison.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: dataset
<p>This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: 2D segmentations
<p>This dataset contains 2D segmentations of images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation algorithm, 3D visualization and geometric analysis are presented in the corresponding manuscript. Files are uploaded in .jpg format and are named: lamella_slicenumber, with slicenumber varying from 1 to 8100. There is also a Matlab file, UndulationData_Zenodo.mat, in which all the relevant variables post analysis are stored. This file contains a variable called "myFiles", which contains the link between the slicenumbers used here and the original dataset that is published in Zenodo (.tif synchrotron images).</p>
ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)
<p>We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.</p>
Data for "Volcano-tectonic interactions at Sabancaya volcano, Peru: Eruptions, magmatic inflation, moderate earthquakes, and fault creep"
<p>Data and models presented in the paper "Volcano-tectonic interactions at Sabancaya volcano, Peru: Eruptions, magmatic inflation, moderate earthquakes, and fault creep". See file "README.txt" for detailed descriptions of each item.</p>
Uplift and Seismicity driven by Magmatic Inflation at Sierra Negra Volcano, Galápagos Islands
<p>Catalogue of detected earthquakes and cGPS uplift timeseries for Sierra Negra Volcano, Galapagos Islands</p>
HPF data for "A Large and Variable Leading Tail of Helium in a Hot Saturn Undergoing Runaway Inflation"
<p>Data from the Habitable Zone Planet Finder (HPF) Spectrograph at McDonald Observatory, in the form of high resolution infrared echelle spectra. The target is HAT-P-67, a planet host star. The spectra were acquired by Queue observations with the Hobby Eberly Telescope in the period 2020-2022. The data were reduced with the "Goldilocks" pipeline. The full dataset is described in detail in the paper "A Large and Variable Leading Tail of Helium in a Hot Saturn Undergoing Runaway Inflation". </p> <p>The abstract for that paper is reproduced below:</p> <div> <div>Atmospheric escape shapes the fate of exoplanets, with statistical evidence for transformative mass loss imprinted across the mass-radius-insolation distribution. Here we present transit spectroscopy of the highly irradiated, low-gravity, inflated hot Saturn HAT-P-67 b. The Habitable Zone Planet Finder (HPF) spectra show a detection of up to 10% absorption depth of the 10833 Angstrom Helium triplet. The 13.8 hours of on-sky integration time over 39 nights sample the entire planet orbit, uncovering excess Helium absorption preceding the transit by up to 130 planetary radii in a large leading tail. This configuration can be understood as the escaping material overflowing its small Roche lobe and advecting most of the gas into the stellar---and not planetary---rest frame, consistent with the Doppler velocity structure seen in the Helium line profiles. The prominent leading tail serves as direct evidence for dayside mass loss with a strong day-/night- side asymmetry. We see some transit-to-transit variability in the line profile, consistent with the interplay of stellar and planetary winds. We employ 1D Parker wind models to estimate the mass loss rate, finding values on the order of 2x10^13 g/s, with large uncertainties owing to the unknown XUV flux of the F host star. The large mass loss in HAT-P-67 b represents a valuable example of an inflated hot Saturn, a class of planets recently identified to be rare as their atmospheres are predicted to evaporate quickly. We contrast two physical mechanisms for runaway evaporation: Ohmic dissipation and XUV irradiation, slightly favoring the latter.</div> </div>
Pneumatic elastostatics of multi-functional inflatable lattices: Realization of extreme specific stiffness with active modulation and deployability
<p>Supplementary codes and data: Elastostatics of multi-functional inflatable lattices: Realization of extreme specific stiffness with active modulation and deployability</p>
Sci-Fi movies by inflation-adjusted gross revenue in the United States.
<p>Sci-Fi movies by inflation-adjusted gross revenue in the United States.</p> <p>The dataset is a .csv file with < ; >used as separator.</p> <p>The dataset has the next columns:</p> <ul> <li>Title: movie title.</li> <li>Release_Year: year of the movie's release.</li> <li>Watchtime: movie duration in minutes.</li> <li>Genre: genre.</li> <li>Movie_Rating: IMDB movie rating.</li> <li>Metascore: metacritic movie rating.</li> <li>Votes: number of ratings.</li> <li>Gross_collection: incomes generated by the movie.</li> <li>Sinopsis.</li> <li>Director.</li> <li>Star: main actors.</li> <li>Gross_equivalent: income generated by movies with dollar devaluation correction.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Food Inflation and Child Health
<p>Data Repository for</p> <p> </p> <p>Woldemichael, A., Kidane, D., and Shimeles, A. 2022. Food Inflation and Child Health. World Bank Economic Review. https://doi.org10.1093/wber/lhac009</p>
Text-fig. 1. Pistacia terrazasae sp. nov., a: UF 279-85025; b–i: UF 279-24545. a: Ring-porous wood with widely spaced solitary earlywood vessels; latewood vessels in radial multiples of 4 or more and in clusters, TS. b: Growth ring boundary, fiber walls thin to thick, TS. c: Simple perforation plates, alternate intervessel pits, helical thickenings in vessels, TLS. d: Multiseriate rays to 4-seriate, tyloses in vessels, helical thickenings throughout body of vessel element, and alternate intervessel pitting, TLS. e: Vessel-ray parenchyma pitting with reduced borders, oval in outline, RLS. f: Marginal row of upright cells, one inflated and crystalliferous, procumbent body cells, RLS. g: Multiseriate rays mostly 3-seriate, occasionally 4-seriate, uniseriate rays usually <10 cells tall, TLS. h: Ray with enlarged crystalliferous marginal cell, to left of C, TLS. i: Ray with canal, TLS. Scale bars: 200 µm in a, g; 100 µm in b, d, h; 50 µm in c, i; 20 µm in e, f. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 1. Pistacia terrazasae sp. nov., a: UF 279-85025; b–i: UF 279-24545. a: Ring-porous wood with widely spaced solitary earlywood vessels; latewood vessels in radial multiples of 4 or more and in clusters, TS. b: Growth ring boundary, fiber walls thin to thick, TS. c: Simple perforation plates, alternate intervessel pits, helical thickenings in vessels, TLS. d: Multiseriate rays to 4-seriate, tyloses in vessels, helical thickenings throughout body of vessel element, and alternate intervessel pitting, TLS. e: Vessel-ray parenchyma pitting with reduced borders, oval in outline, RLS. f: Marginal row of upright cells, one inflated and crystalliferous, procumbent body cells, RLS. g: Multiseriate rays mostly 3-seriate, occasionally 4-seriate, uniseriate rays usually <10 cells tall, TLS. h: Ray with enlarged crystalliferous marginal cell, to left of C, TLS. i: Ray with canal, TLS. Scale bars: 200 µm in a, g; 100 µm in b, d, h; 50 µm in c, i; 20 µm in e, f.
Inflation Reduction Act Energy Communities
<p>The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:</p> <ol> <li>a brownfield site,</li> <li>a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment <em>or</em> (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; <em>and</em> an unemployment rate at or above the national average for the previous year, or</li> <li>a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.</li> </ol> <p>These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:</p> <ol> <li>Brownfields – Source: <a href="https://www.epa.gov/frs/geospatial-data-download-service">EPA</a>. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).</li> <li>Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census <a href="https://www.census.gov/programs-surveys/cbp.html">County Business Patterns</a>. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).</li> </ol> <p>--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.</p> <p>--Relevant columns include:</p> <p> a) SUM_nhgis0: Total employment in 2020.</p> <p> b) SUM_nhgis1: Total unemployment in 2020.</p> <p> c) P_Unemp: Percent unemployment in 2020.</p> <p> d) Q_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.</p> <p> e) FF_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.</p> <p> f) final_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.</p> <ol> <li>Retired Power Plants – Source: EIA via <a href="https://hifld-geoplatform.opendata.arcgis.com/maps/ee0263bd105d41599be22d46107341c3/about">HFLID</a>. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.</li> </ol> <p>--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.</p> <ol> <li>Abandoned Coal Mines – Source: <a href="https://www.msha.gov/mine-data-retrieval-system">MSHA</a>. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.</li> </ol> <p>--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.</p> <p>5) US State Borders– Source: <a href="https://data2.nhgis.org/main">IPUMS NHGIS</a>.</p> <p> </p> <p>Also included here are polygon shapefiles for Onshore <a href="https://zenodo.org/record/5021146#.Y0XbRnbMK39">Wind and Solar Candidate Project Areas</a> from <a href="https://repeatproject.org/">Princeton REPEAT</a>. These files have been updated to include columns related to the energy communities.</p> <p>New columns include:</p> <ol> <li>CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.</li> <li>CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.</li> <li>FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.</li> <li>UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.</li> <li>MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.</li> <li>P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.</li> </ol>
Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model
<p>this dataset are “ssociation of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model” Supplementary Material.</p>
Wind Tunnel Testing of Tethered Inflatable Wings
<p>This dataset consists of all the data collected and presented in the AIAA Journal of Aircraft titled "Wind Tunnel Testing of Tethered Inflatable Wings". The attached zipped folder contains a README file that explains the dataset. </p>
Dataset: Vanguard Short-Term Inflation-Protected Securities Index Fund ETF Shares (VTIP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Alpha Architect High Inflation And Deflation ETF (HIDE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Linked collectors and determiners for: Review of Poecilimon species with inflated pronotum: description of four new taxa within an acoustically diverse group.
Natural history specimen data linked to collectors and determiners held within, "Review of Poecilimon species with inflated pronotum: description of four new taxa within an acoustically diverse group". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/fdd9bfb7-b663-48a4-bb92-9842a8820d64">https://bionomia.net/dataset/fdd9bfb7-b663-48a4-bb92-9842a8820d64</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/fdd9bfb7-b663-48a4-bb92-9842a8820d64">https://gbif.org/dataset/fdd9bfb7-b663-48a4-bb92-9842a8820d64</a>. Formatted as a Frictionless Data package.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.